In this paper, we present a methodology for
monitoring the underwater light climate for aquatic vegetation based on
spectral reflectance above the water surface. This method is composed by three consequences
steps:

First, based on the semi-physical
bio-optical model, we built a water reflectance model. This model relates the
inherent optical properties (IOP) of water to the apparent optical properties
of the lake water and thus can simulate different spectral remote sensing reflectances
above the water surface by different combinations of water constituents. This
step is called “forward process”. In this study, the water reflectance model
gives satisfactory simulation on most cases.

Then in the second step, an artificial
neural network is trained by those simulations, and thus can

retrieve concentrations of water
constituents by inverse the water reflectance model. This step is

called “inverse process”. By this method,
the ANN-based algorithm can retrieve the concentrations of SPM, CHL and CDOM at
the same time. And the accuracies are acceptable (R2=0.758, 0.741
and 0.389 for SPM, CHL and CDOM concentrations respectively).

In the third step, an underwater light
climate model was built to predict the light climate at the bottom of the lake,
where aquatic vegetation grows, according to the concentrations of water
constituents retrieved in step two and the water depth.

The advantage of this method, comparing
with empirical methods, is we don’t need to collect a lot of water samples to
build the regression formula for retrieving the concentrations of water
constituents. And it is more site-independent.